In this project, I have utilized survival analysis models to see how the likelihood of the customer churn changes over time and to calculate customer LTV. I have also implemented the Random Forest model to predict if a customer is going to churn and deployed a model using the flask web app.

Customers in the telecom industry can choose from a variety of service providers and actively switch from one to the next. With the help of ML classification algorithms, we are going to predict the Churn.

Unlock actionable insights and boost customer retention with this Power BI project. Analyze and visualize risk factors to proactively prevent churn. ➡️

End to end projects-- Customer Churning prediction using Gradient Boost Classifier Algorithm perform pre-processing steps then fit data into the Algorithm and Hyper Parameter Tunning to reduce TN & FN value to perform our model to works with a new data. Finally deploying the model using Flask API

Predict and prevent customer churn in the telecom industry with our advanced analytics and Machine Learning project. Uncover key factors driving churn and gain valuable insights into customer behavior with interactive Power BI visualizations. Empower your decision-making process with data-driven strategies and improve customer retention.

The Customer Churn table contains information on all 7,043 customers from a Telecommunications company in California in Q2 2022. We need to predict whether the customer will churn, stay or join the company based on the parameters of the dataset.

Small projects with Deep Learning magic! - Predicting Customer Churn in Banking, Predict tags on Stack Overflow, Sign Language Recognition

Analysis and Prediction of the Customer Churn Using Machine Learning Models (Highest Accuracy) and Plotly Library

This project aims to predict customer churn using machine learning techniques. By analyzing historical customer data, the model identifies patterns that indicate whether a customer is likely to leave. This can help businesses take proactive measures to retain customers and reduce churn rates.

Customer churn prediction using Neural Networks with TensorFlow.js

Telco Churn Analysis and Modeling is a comprehensive project focused on understanding and predicting customer churn in the telecommunications industry. Utilizing advanced data analysis and machine learning techniques, this project aims to provide insights into customer behavior and help develop effective strategies for customer

Transforming CRM using AI/ML

Data Science Projects done at Data Trained Education during PG Data Science & ML Course

Extensive EDA of the IBM telco customer churn dataset, implemented various statistical hypotheses tests and Performed single-level Stacking Ensemble and tuned hyperparameters using Optuna.

The objective is to build a classifier for prediction of customer churn.

Step-by-step EDA and data preprocessing journey for customer churn prediction. Updated weekly with raw & processed datasets, notebooks, and ML-ready pipeline.

A fully automated ML pipeline for customer churn prediction in telecom, orchestrated with Apache Airflow. Covers data ingestion, validation, feature engineering, model training, deployment, and monitoring with DVC-based versioning for complete reproducibility.

An end-to-end Machine Learning and Agentic AI system to predict customer churn and generate structured retention strategies using LangGraph and Open-Source LLMs.

Marketing Analytics

Stanford Continuing Studies course "Data-Driven Marketing" by Angel Evan, Consultant. Completed Winter 2017-2018

Customer Churn Prediction using PyCaret.

No description provided.

Customer Churn Prediction is a machine learning project that analyzes telecom customer data to predict which users are likely to stop using the service. By identifying the key factors that lead to churn and comparing different models, this project helps businesses take proactive steps to retain customers and reduce revenue loss.

A machine learning project that predicts customer churn using classification algorithms such as KNN, SVC, Logistic Regression, Decision Tree, and Random Forest. Includes data analysis, preprocessing, visualization, model comparison, and a CLI prediction interface with saved models.

Machine-Learning-1

Production-ready customer churn prediction API built with FastAPI, scikit-learn, and Docker.

Customer-Churn-ANN

Machine Learning project to predict customer churn using Decision Tree and XGBoost

The goal of this project was to utilize classification models to predict whether or not a customer would churn. I went through the entire machine learning pipeline, discovered drivers of churn, and created many different models. Ultimately, my best Random Forest Classifier model was able to predict churned customers with an accuracy of about 80%.

Predecir el abandono de futuros clientes